Impartial Multi-task Representation Learning via Variance-invariant Probabilistic Decoding (2025.acl-long)
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| Challenge: | Existing methods focus on balancing loss or gradients but fail to address this issue due to the representation discrepancy in latent space. |
| Approach: | They propose a framework that harmonizes representation spaces across tasks to ensure impartial learning by harmonizing representation spaces. |
| Outcome: | The proposed framework outperforms 12 representative methods under the same multi-task settings, especially in heterogeneous task combinations and data-constrained scenarios. |
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| Challenge: | AdapterShare is an adapter differentiation method to explicitly model the task correlation among multiple tasks. |
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| Challenge: | Multi-task learning (MTL) for generalist models is a promising direction that offers transfer learning potential. |
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